Courses

Topics in Statistical Data Science

Last updated: 2025-07-29

THIS IS JUST RANDOM INITIAL THOUGHTS AND NOTHING IS FINALIZED YET.

Overview

This course will cover current topics in Data Science from a statistical perspective. The exact topics will vary from year to year. Emphasis will be on practical aspects of data science. This could include tools, workflows, reproducibility and communication through a statistical lens but not all topics will be covered exhaustively every year.

The exact content will vary year to year but will include practical aspects of data science such as such as tools, workflows, reproducibility and communication. Topics will be taught through a statistical lens and the emphasis and content will vary from year to year. By the end of the course students will (i) have a better understanding of current tools and practices in data science, (ii) will have learned to effectively communicate their findings to a non-statistical audience and (iii) be able to critically evaluate the use of techniques they learned from a statistical viewpoint.

Learning objectives

The purpose of the course is to develop the skills needed to effectively do data science using modern tools, with a strong statistical focus. By the end of the course, you should be able to:

Content

Part 1: Technical skills

Week 1 "Research Software Engineering"

Learning outcomes:

  1. Know how and why to pull, commit, add, and push using Git and GitHub. Be comfortable making and receiving Pull Requests, and creating and closing Issues using GitHub to work as a team.
  2. Know how, when, and why to use CSVs and Parquet.

Material:

Week 2 "Using Python for data science"

Learning outcomes:

  1. Have a good working knowledge of how to use Python to do data science, using VS Code, UV and Ruff.

Material:

Week 3 "DevOps for data science"

Learning outcomes:

Material:

Week 4

Part 2: Statistical workflow

Week 5 "Written and oral communication"

Learning outcomes:

Material:

Week 6 "Data gathering and validation"

Learning outcomes:

Material:

Week 7 "Data management"

Learning outcomes:

Material:

Week 8 "Model interpretation"

Learning outcomes:

Material:

Task idea:

Week 9

Part 3: Integrating AI

Week 10 "Python with AI"

Week 11 "Developing benchmarks"

Week 12 "Deploying AI"

Assessment

Using AI is encouraged in the course, but you still need to know the underlying material, in the same way that you still need to know how to add even though everyone has calculators. As such, assessment is divided into "secure" and "insecure" based on whether you will have access to AI to help you. The maximum that you can receive overall is the proportion that you get on secure assessment. For instance, if you got 100% on secure assessment, then your grade would be worked out based on the weights below. But if you only got 50% on secure assessment, then even if you got 100% on everything else your overall grade would be 50%.

Donaldson paper

In-class presentation

Weekly online quiz

Mid-term

Final paper

Final exam